Adjustments for CRAN upload

[valse.git] / reports / essaiPlot.R

diff --git a/reports/essaiPlot.R b/reports/essaiPlot.R
index dea7e00..10b0e01 100644 (file)
--- a/reports/essaiPlot.R
+++ b/reports/essaiPlot.R
@@ -1,24 +1,37 @@
 ### Regression matrices
-model = res_valse
+model = Res
 K = dim(model$phi)[3]
 valMax = max(abs(model$phi))
 
 require(fields)
+
 if (K<4){
   par(mfrow = c(1,K))
-} else par(mfrow = c(2, (K+1)/2))
+} else op = par(mfrow = c(2, (K+1)/2))
+
+## Phi
 
 for (r in 1:K){
-  image.plot(t(abs(model$phi[,,r])), 
+  image.plot(t(abs(model$phi[,,r])),
              col=gray(rev(seq(0,64,length.out=65))/65),breaks=seq(0,valMax,length.out=66))
 }
+par(mfrow = c(1,K),oma = c(0,0,3,0))
+mtext("Regression matrices in each cluster", side=3, line=4, font=2, cex=2, col='red')
+
+par(mfrow = c(1,2), oma=c(0,0,3,0))
+for (i in 1:4) 
+  plot(runif(20), runif(20), 
+       main=paste("random plot (",i,")",sep=''))
+par(op)
+mtext("Four plots", 
+      side=3, line=4, font=2, cex=2, col='red')
 
 ### Zoom onto two classes we want to compare 
 kSel = c(1,2)
 par(mfrow = c(1,3))
 
 for (r in kSel){
-  image.plot(t(abs(model$phi[,,r])),title="hat{beta}",xaxt="n",yaxt="n", 
+  image.plot(t(abs(model$phi[,,r])),xaxt="n",yaxt="n", 
              col=gray(rev(seq(0,64,length.out=65))/65),breaks=seq(0,valMax,length.out=66))
 }
 image.plot(t(abs(model$phi[,,kSel[1]]-model$phi[,,kSel[2]])), 
@@ -35,7 +48,7 @@ for (r in 1:K){
 Gam = matrix(0, ncol = K, nrow = n)
 gam  = Gam
 for (i in 1:n){
-  for (r in 1:k){
+  for (r in 1:K){
     sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
     Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
   }
@@ -57,4 +70,4 @@ for (r in 1:K){
   meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
 }
 
-matplot(meanPerClass, type='l')
\ No newline at end of file
+matplot(meanPerClass, type='l')